Project Summary/Abstract In recent years, health care systems and health care providers have made concerted efforts to practice evidence based medicine to provide patients with the best available information when making choices about their medical decisions. However, these decisions are often complex with many uncertainties and potential outcomes ? some beneficial others dire. A popular tool used to help identify best treatment strategies is a decision tree, which outlines a patient's potential outcomes given an initial medical choice. Complex decision trees are evaluated via Monte Carlo microsimulation to trace a patient's path through the tree. This movement is inherently stochastic because outcomes are probabilistic; however, when the microsimulation is repeated many times, it provides the probability of each associated outcome resulting from the initial medical decision. From this probability distribution, quantitative measures associated with each medical decision can be calculated including beneficial as well as adverse events, life years, quality-adjusted life years (a generic measure of disease burden), and others. When outcome costs are known and incorporated into the model, cost-effectiveness analysis (CEA) can be used to readily compute the relative costs, effectiveness, and incremental cost-effectiveness for each health outcome. We propose to add functionality to the mathematical modeling software Berkeley Madonna to allow users to build decision trees and carry out Monte Carlo microsimulations on these trees (Aim 1). Berkeley Madonna's interface was designed to gently introduce students from non-technical fields into mathematical modeling by using a simple syntax and graphical images to construct sophisticated equations. We will leverage this easy-to-use interface to introduce medical researchers to microsimulation. The software will be adapted to build decision trees with built-in functions and customized graphics specific to this field, including measures from CEA. Patients moving through a decision tree using microsimulation must be simulated hundreds of thousands to millions of times to arrive at statistically significant outcome probabilities, and these simulations are computationally intense often requiring months of computer time. We will harness the power of graphics processing units (GPUs) to parallelize these simulations to achieve tremendous speedups compared to commercially available software, which have not taken advantage of these hardware capabilities. We show that a simple Monte Carlo microsimulation can be simulated 700x faster on a GPU compared to a CPU, and we will optimize the code to fully realize these speedups when simulating complex decision trees (Aim 2). Successful completion of our goals will provide a powerful research tool to the medical decision making field, which will positively impact health outcomes research. We expect that our software will be particularly beneficial to the cardiovascular research community, which has a history of practicing evidence-based medicine that includes simulation modeling.